Discover Algorithms for Reward-Based Learning in R [Video]

Discover Algorithms for Reward-Based Learning in R [Video]

Dr. Geoffrey Hubona

Learn how to utilize algorithms for reward -based learning, as part of Reinforcement Learning with R
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Video Description

Users will be taken through a journey that starts by showing them the various algorithms that can be used for reward-based learning. The video describes and compares the range of model-based and model-free learning algorithms that constitute RL algorithms.

The Course starts by describing the differences in model-free and model-based approaches to Reinforcement Learning. It discusses the characteristics, advantages and disadvantages, and typical examples of model-free and model-based approaches.

We look at model-based approaches to Reinforcement Learning.We discuss State-value and State-action value functions, Model-based iterative policy evaluation, and improvement, MDP R examples of moving a pawn, how the discount factor, gamma, “works” and an R example illustrating how the discount factor and relative rewards affect policy. Next, we learn the model-free approach to Reinforcement Learning.This includes Monte Carlo approach, Q-Learning approach, More Q-Learning explanation and R examples of varying the learning rate and randomness of actions and SARSA approach. Finally, we round things up by taking a look at model-free Simulated Annealing and more Q-Learning algorithms.

The primary aim is to learn how to create efficient, goal-oriented business policies, and how to evaluate and optimize those policies, primarily using the MDPtoolbox package in R. Finally, the video shows how to build actions, rewards, and punishments with a simulated annealing approach.

Style and Approach

In this course, you will start by seeing what Model-Free and Model-Based approaches can do for them with the help of real world examples. Finally, the user will get to build actions, rewards, and punishments through these models in R for reinforcement learning

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What You Will Learn

  • Learn R examples of policy evaluation and iteration
  • Implement typical applications for model-based and model-free RL
  • Understand policy evaluation and iteration
  • Execute Environment and Q-Learning functions with R
  • Learn Episode and state-action functions in R
  • Master Q-Learning with Greedy Selection Examples in R
  • Master the Simulated Annealing Changed Discount Factor through examples in R

Authors

Video Details

ISBN 139781788474092
Course Length2 hour 36 minutes
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